meta data can then process that meta-knowledge to achieve “meta-cognition” and build an intelligence infrastructure. (Don’t worry, I come down to earth in the next few questions.) What is the difference in focus between your consumer and enterprise products? For a consumer, the focus is to save time, assist him or her in managing a knowledge base, and filling in the knowledge gaps. For an enterprise, it comes down to benefiting from those individuals using the system. There is a focus on the value of a
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meta data over 800 members. MDC: Meta Data Coalition. A consortium founded in 1995 with close to 50 vendors and end-users whose goal is to provide a tactical solution for metadata exchange. CORBA: Common Object Request Broker Architecture. A standard from the OMG for communicating between distributed objects (objects are self-contained software modules). CORBA provides a way to execute programs (objects) written in different programming languages running on different platforms (i.e. UNIX, mainframe, Windows), no
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meta data are members of the Meta Data Coalition (MDC) which has proposed the Open Information Model as a standard. In April of 1999, the MDC and the OMG announced a cooperative effort to develop metadata standards. However, we find it odd that the MDC is not mentioned in this press release, nor are the vendor members of the MDC. A standardized approach to metadata would be very much in the customer's interest. However, in order for it to benefit users, all of the major vendors must adopt the same standard. We
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meta data data mart building, integrated meta data modeling, and integrated security as well as content management and distribution. The product is a suite that contains: Extract/Transform/Load capabilities for populating data marts. This technology was acquired with the purchase of U.K. based Relational Matters, which created the DecisionStream product, the first OLAP-aware data integration product . DecisionStream is an ETL tool capable of populating OLAP hypercubes, such as the one used by Cognos PowerPlay.
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meta data Evolution of a Real-time Data Warehouse The Evolution of a Real-time Data Warehouse Jorge Garcia - December 23, 2009 Understanding Real-time Systems Today, real time computing is everywhere, from customer information control systems (CICSs) to real-time data warehouse systems. Real-time systems have the ability to respond to user actions in a very short period of time. This computing behavior gives real-time systems special features such as instant interaction: users request information from the system
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meta data generation Data Auditing for Data Breach Protection and Risk Mitigation Data breaches and leaks are on the rise—and the consequences, from theft of identity or intellectual property, can seriously compromise a company’s reputation. Stolen laptops, hacking, exposed e-mail, insider theft, and other causes of data loss can plague your company. How can you detect (and respond!) to breaches and protect your data center? Learn about the functions and benefits of an automated data auditing system. BEGINLY
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meta data Visualization: When Data Speaks Business For many organizations, data visualization is a practice that involves not only specific tools but also key techniques, procedures, and rules. The objective is to ensure the best use of existing tools for extending discovery, gaining knowledge, and improving the decision-making process at all organizational levels. This report considers the important effects of having good data visualization practices and analyzes some of the features, functions, and advantages
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meta data Migration Management: A Methodology to Sustaining Data Integrity for Going Live and Beyond For many new system deployments, data migration is one of the last priorities. Data migration is often viewed as simply transferring data between systems, yet the business impact can be significant and detrimental to business continuity when proper data management is not applied. By embracing the five phases of a data migration management methodology outlined in this paper, you can deliver a new system with quali
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2013 Big Data Opportunities SurveyWhile big companies such as Google, Facebook, eBay, and Yahoo! were the first to harness the analytic power of big data, organizations of all sizes and industry

meta data Big Data Opportunities Survey While big companies such as Google, Facebook, eBay, and Yahoo! were the first to harness the analytic power of big data, organizations of all sizes and industry groups are now leveraging big data. A survey of 304 data managers and professionals was conducted by Unisphere Research in April 2013 to assess the enterprise big data landscape, the types of big data initiatives being invested in by companies today, and big data challenges. Read this report for survey responses
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Achieving a Successful Data MigrationThe data migration phase can consume up to 40 percent of the budget for an application implementation or upgrade. Without separate metrics for migration, data

meta data lineage capability, such as metadata management. Data profiling, validation, and cleansing are also important. In application environments data quality is also about business rules. Data must obey to unique business rules, as well as meet validation thresholds. Use Case Scenario Consolidate Systems Need: Simplify parts, materials, and supplies by migrating data from 15 incompatible mainframe and legacy systems to SAP R/3. Approach: Leverage data integration platform with easy-to-use development tools,
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meta data ability to generate a metadata repository from actual data instead of data dictionaries, IMS PSB's, etc. is a strong and valuable capability. It is estimated that approximately 80% of the world's data is stored in mainframe files, a great percentage of that is VSAM. Many legacy applications had no data validation routines (it was common for data entry clerks to type anything into a field where they didn't know the correct value, just so they could get on to the next field). The product's capabilities to
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meta data Loss Prevention Best Practices: Managing Sensitive Data in the Enterprise While a great deal of attention has been given to protecting companies’ electronic assets from outside threats, organizations must now turn their attention to an equally dangerous situation: data loss from the inside. Given today’s strict regulatory standards, data loss prevention (DLP) has become one of the most critical issues facing executives. Fortunately, effective technical solutions are now available that can help. BEG
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meta data Big Data Actionable: How Data Visualization and Other Tools Change the Game To make big data actionable and profitable, firms must find ways to leverage their data. One option is to adopt powerful visualization tools. Through visualization, organizations can find and communicate new insights more easily. Learn how to make big data more actionable by using compelling data visualization tools and techniques.
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